docs: def z_cost(z, errors, mean, std):
      """Compute how bad a z value is.
  
      The original formula is::
  
                   (delta_mean/mean) + (delta_std/std)
          ------------------------------------------------------
          number of errors above + (number of sequences above)^2
  
      which computes the "goodness" of `z`, meaning that the higher the value
      the better the `z`.
  
      In this case, we return this value inverted (we make it negative), to convert
      it into a cost function, as later on we will use scipy to minimize it.
      """
  
      epsilon = mean + z * std
  
      delta_mean, delta_std = deltas(errors, epsilon, mean, std)
      above, consecutive = count_above(errors, epsilon)
  
      numerator = -(delta_mean / mean + delta_std / std)
      denominator = above + consecutive ** 2
  
      if denominator == 0:
          return np.inf
  
      return numerator / denominator